Quantitative Research Methods – Introduction to Applied Statistics

Second meeting

• Why we need R in IR
• R environment

• Ethnic Minorities and the Clash of Civilizations: A Quantitative Analysis of Huntington's Thesis

• An article by JONATHAN FOX (2002), that uses the Minorities at Risk dataset to provide a quantitative element to the civilizations debate

• To find, whether there has been a rise in both the quantity and intensity of ethnic conflicts between groups belonging to different civilizations since the end of the Cold War.

• To verify Huntington's Thesis using quantitative methods

• To provide working tools for theory validation

• Ever since Huntington proposed his “clash of civilizations” thesis there has been a vigorous debate over its validity.

• Controversial argument that the end of the Cold War resulted in a change in the nature of world conflict

• Huntington ignored some important phenomenon that will impact on conflict, thereby making his theory irrelevant.
  • Improved conflict management techniques
  • World wide trends toward secularism
  • Information technology
  • Ethnopolitical conflicts result from protracted discrimination rather than cultural roots

• Huntington ignored some important phenomenon that will impact on conflict, thereby making his theory irrelevant.

• Pfaff (1997) accuses Huntington of ignoring facts.

• Hassner (1997), even goes as far as to accuse Huntington of bending the facts to fit his theory.

• The most of Huntington's critics as well as Huntington himself, rely mostly on anecdotal evidence.

• To assess quantitatively whether some of Huntington's predictions are correct with regard to ethnic conflict

• To identify if clashes between minority and majority groups of different civilizations within the state are more common and more intense than those between groups who are both of the same civilization

• To prove that Huntington was wrong, using STATISTICAL EVIDENCE

• Minorities at Risk Phase 3 (MAR3) dataset

• The unit of analysis in this dataset is the minority group within a state.

• For each of the 275 cases there is a minority and a majority group.

• Comparing mean scores of the dataset using Student's T-test

• The results of this analysis do not support Huntington's “clash of civilizations”“ argument.

• Civilizational conflicts constitute a minority of ethnic conflicts both during and after the Cold War.

• In fact, the largest percentage of ethnic conflicts occur within civilizations

• Simplistic hypothesis testing is bad for International Relations

• Why validating the theories using the data matters

• T-test

eList<-c("I'm the first element!", "I'm the second one", "I'm the third one")

eList[1] ### First element

eList[2] ### Second element

eList[3] ### Third element

logical<-c(TRUE, FALSE, T, F)
integer<-c(1L, 4L, 8L, 0L)
double<-c(1, 3.6, 90, 4)
characters<-c("a", "b", "სამი", "four")

simpleList<-list(TRUE, FALSE, T, F)
combiList<-list(c("a", "b"), c("იყო არაბეთს როსტევან"), c(1L, 4L, 8L, 0L), 98)
multidimList<-list(list(c("a", "b"), 7), c("რომელმან შექმნა სამყარო"))

attr(x, "Attribute") <- "Attribute"

attributes(x)

myObject<- c(10, 20, 30)
names(myObject) <- c("Age", "Height", "Weight")

eyes<-factor(c(1, 2, 3, 4), levels=c("Brown", "Green", "Blue", "Grey"))

a<-c(1, 2)
b<-c(10, 20, 30, 40, 50, 60)

ab.array<-array(c(a, b), dim=c(3, 3, 2))
print(ab.array)

a<-c(1, 2)
b<-c(10, 20, 30, 40, 50, 60)
column.name <-c("Column1", "Column2", "Column3")
row.name <- c("Row1", "Row2", "Row3")
matrix.name<- c("Thomas Anderson", "Neo")
ab.array <- array(c(a,b),dim = c(3,3,2),dimnames = list(row.name, column.name, matrix.name))
print(ab.array)

my.matrix <- matrix(c(1:15), nrow=5)
print(my.matrix)

### You could only indicate the number of columns:
my.matrix <- matrix(c(1:15), ncol=5)
print(my.matrix)

col.names <- c("First", "Second", "Third", "Fourth", "Fifth")
row.names <- c("Row1", "Row2", "Row3")
my.matrix <- matrix(c(1:15), ncol=5, dimnames=list(row.names, col.names))
print(my.matrix)

df <- data.frame(
   my.id = c (1:11), 
   region.name = c("Samegrelo-Zemo Svaneti","Racha-Lechkhumi-Kvemo Svaneti","Imereti","Guria","Adjara", "Samtskhe-Javakheti", "Shida Kartli", "Kvemo Kartli", "Mtskheta-Mtianeti", "Tbilisi", "Kakheti"),
   value.added = c(1807.4, NA, 2685.2, 584.4, 2039.7, 724.6, 1485.21, 2162.9, NA, 12147.1, 1459.1),
   stringsAsFactors = FALSE
    )
print(df)

x <- c(10, 2, 45, 7, 89, 11)
abs(x)
sqrt(x)
log(x)
log10(x)
exp(x)
sum(x)
median(x)
mean(x)
max(x)
min(x)

df <- data.frame(
   my.id = c (1:11), 
   region.name = c("Samegrelo-Zemo Svaneti","Racha-Lechkhumi-Kvemo Svaneti","Imereti","Guria","Adjara", "Samtskhe-Javakheti", "Shida Kartli", "Kvemo Kartli", "Mtskheta-Mtianeti", "Tbilisi", "Kakheti"),
   value.added = c(1807.4, NA, 2685.2, 584.4, 2039.7, 724.6, 1485.21, 2162.9, NA, 12147.1, 1459.1),
   stringsAsFactors = FALSE
    )

df[which(df$region.name == "Shida Kartli"), ] # Select only Shida Kartli

df[which(df$value.added > 1000), ] # Select those regions producing added value of more than 1 million GEL